Hysteresis refers to the asymmetric path between to alternative states. Specifically, we apply the concept to analyse differences between degradation and regeneration processes, a phenomena pointed out in the dynamics of several ecosystems [1]. The topic is quite relevant for the ongoing debate about the irreversibility of desertification, challenged by different studies that support re-greening in degraded areas [2].

It is possible to deduce the existence of hysteresis through the speed of the aforementioned opposed processes. For that, a statistically sound analysis based on 2dRUE results [3] has been implemented. Previously, 2dRUE was applied to assess land condition in China Drylands using time series NPP data computed from Envisat Meris images [4]. The method incorporates a stepwise regression to signify the effects of time and aridity on vegetation. This allows distinguishing if biomass changes are explained by the impact of wet and dry years, or by land degradation itself. In this way, 2dRUE supplies standard partial regression coefficients, an explicit and untainted measure of changes in land condition.

We have selected the Mann-Whitney U test to compare two variables: Positive and negative time regression coefficients, i.e. regeneration and degradation do not related with aridity fluctuations. The null hypothesis is that both processes happen at the same rate. Data is divided into 3 categories, each one with a different number of classes, Land-use (20); 2dRUE Assessment (8); FAO Aridity (3). Exploratory results show that in half the cases there are significant differences between the speed of degradation and regeneration. In most of them, the pace of regeneration is higher than degradation. Forthcoming works require a detailed insight within those significant categories in aim to interpret the scope of our preliminary results.

Oral presentation

Identification of Land Degradation by Coupling Vegetation and Climate based on Remote Sensing Data

Land degradation is a process by which the land productive capacity declines or even is completely lost under the influence of natural forces and human activities. The scope of land degradation has become global in the last decades, which compromises sustainable land management and threatens the safety of food production, especially in the poverty-stricken areas of developing countries. Desertification is one kind of land degradation and mainly occupied in arid, semi-arid and semi-humid areas. China is one of the most seriously affected countries by desertification. By the end of 2014, the desertified land area of China was 2.61×106km2. Post-hoc mitigation approaches are expensive and often ineffective. Therefore early warning systems based on Earth Observation make the most accepted scientific basis for controlling land degradation.

With the development of remote sensing technology, long time series remote sensing data have been available for land degradation assessment and monitoring, and the vegetation indicators, such as the NDVI, NPP, Vegetation coverage and biomass were commonly used. However, time series vegetation index will fluctuate severely due to the impact of climate change, especially the fluctuation of annual precipitation, thereby the land production capacity could not be determined accurately.

Therefore, to solve the problem, Xilin Gol League, In+-ner Mongolia Autonomous Region, China, where the land degradation is prevailing in the first decade of the 21st century was selected as the study area. Based on the annual NPP dataset estimated by 10-Day composite NDVI from Envisat-Meris data at 1.2km resolution during 2003 to 2013 and the same period meteorological raster dataset, a new Moisture-responded Net Primary Productivity (MNPP) method, for identifying areas of land degradation based on the change of annual NPP and MNPP over time and Moisture Index (MI) was developed. It was expected that provide technical support and scientific reference data for land degradation assessment and monitoring in study area, even in the whole drylands in China.

Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots because of the stronger nonlinear spectral mixture effects. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, nonlinear spectral mixing effects for Nitraria shrubs and Haloxylon were validated to be different, additional research is necessary to validate their performance from the canopy to the landscape scale.